video1.0<iframe src="https://www.loom.com/embed/d5392168bb61436aa1fe0314bf97d7c0" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/d5392168bb61436aa1fe0314bf97d7c0-b7c5f2d1734b94c3.gif336.075Hi, I’m presenting my Applied AI final project, SuperSickMusicFinder9000, which upgrades my original music recommender into a full AI system. I built it from core files in the repo, and it runs end to end to recommend songs based on inputs like mood, genre, and activity, with a confidence percentage. I demoed happy workout Pop, sad studying hip hop, and energetic partying EDM, showing the matches and how confidence changes. I also included a guardrail for missing or weak inputs and reliability testing that the system passes. No action is specifically requested from viewers.